Martina Morris & Jeanette Birnbaum
Use CD4 at diagnosis to increase the precision of undiagnosed HIV estimates from the testing history model
Why
The testing history model relies heavily on the inter-test interval, and the distribution of infection probability across that interval.
High CD4 measured at diagnosis is an indicator of recent infection
How
Identify typical times from infection associated with CD4 counts from the research literature;
Use this to reallocate the probability of infection within long infection windows.
NOTE: we only reallocate from a uniform distribution towards more recent infection when indicated by higher CD4. We do not reallocate towards a less recent infection when indicated by low CD4. So the potential impact on the undiagnosed estimate is only possible in one direction: to reduce the undiagnosed fraction.
Impact in WA
Modest reduction in estimates of undiagnosed cases (about 10%)
Consistent with the findings from the previous analysis incorporating the BED for recent infection, but with more confidence because the level of missing data is much lower for CD4 (25% vs. 70% for the BED)
But we need to be careful now because our key remaining assumptions may be leading to downward bias in our estimates.
Introduction
Review of the Testing History method for estimating undiagnosed cases of HIV
Review of key testing history data descriptives in WA State
How we integrate CD4 data into the method
Results
CD4 data descriptives in WA State
Impact on estimated median time since infection, and time spent undiagnosed
Impact on estimated undiagnosed cases in 2014
Distributes probability of infection uniformly across the possible infection window
The hazard (instantaneous rate) of infection at any point within the window is 1/(window length), shown by the red line for each window lengths. Time = 0 refers to time of diagnosis.
The cumulative probability of diagnosis is the area under the curve. The 2-year window assigns greater probability of infection within the last year than the 5-year window, shown by the grey shaded region.
The age distribution of never-testers will influence the window lengths they contribute to the analysis.
Testing histories = Tested + Never Tested
Total N = 5148
With testing history N = 3016.
66% of MSM versus 43% of non-MSM have testing histories
MSM comprise about 2/3 of cases.
What is driving differential response rates?
Labels indicate bounds, e.g. (0,1] includes windows >0 and <= 1. The 18-year windows are never-testers (NT).
These are the cases with the greatest potential for CD4 modification, but only if their CD4 counts at diagnosis are high.
Less than half of these cases are never-testers (13/31 = 42%).
Percent of cases with a concurrent HIV/AIDS diagnosis by group.
Mean age at diagnosis among never-testers is 36 in MSM vs. 42 in non-MSM
This will also lead to shorter windows for MSM.
Whether it's using BED, CD4, or concurrent AIDS Dx, etc
Longer windows of possible infection for non-MSM = more uncertainty regarging time of infection, more opportunity to reallocate when these measures indicate recent infection and to improve the precision of our estimates.
Short testing intervals in MSM = low uncertainty regarding time of infection, less potential impact of additional information
Increasing precision will not necessarily decrease undiagnosed estimates
Modified estimates will deviate from the Base Case only to the extent that these added biomarkers indicate recent infection
An example where this could happen is if cases with long windows tested due to risky exposure: CD4 should help pick up on that, and this would reduce the undiagnosed estimates.
Overall estimates may not change much, but our confidence in them will increase
We expect individual CD4 trajectory after HIV infection is something like this:
With CD4 steadily falling over time in the absence of treatment.
The real picture is more like this:
CD4 progressions are highly variable across individuals.
The standard measure is the number of years it takes for 50% of untreated cases to reach a CD4 level threshold:
| CD4 Threshold | Lodi 2011 | Cori 2015 | We use* |
|---|---|---|---|
| 500+ | 1.3 | 2.3 | 1.5 |
| 350 | 4.3 | 4.2 | 4.0 |
| 200 | 7.9 | 8.0 | 8.0 |
| < 200 | 11.5 | 9.0** |
* Nearest rounded median times that are consistent with both sources. ** For CD4 < 200, we retain our maximum-window assumption of 18 years, which implies a median time of 9 years.
Interpretation: 50% of infection probability should occur within the median time.
Implication: If a case has CD4 > 500 at diagnosis, for example, we allocate 50% of the infection probability to the 1.5 years prior to Dx, and the remaining 50% to the rest of the window.
Lodi S, Phillips A, Touloumi G, Geskus R, Meyer L, Thiébaut R, et al. Time from human immunodeficiency virus seroconversion to reaching CD4+ cell count thresholds <200, <350, and <500 Cells/mm3: assessment of need following changes in treatment guidelines. Clin Infect Dis Off Publ Infect Dis Soc Am. 2011 Oct;53(8):817–25
Cori A, Pickles M, van Sighem A, Gras L, Bezemer D, Reiss P, et al. CD4+ cell dynamics in untreated HIV-1 infection: overall rates, and effects of age, viral load, sex and calendar time. AIDS Lond Engl. 2015 Nov 28;29(18):2435–46.
Example: re-allocating infection probability for an 18 year window
Example: re-allocating infection probability for an 18 year window
Base Case: Uniform distribution, so 50% is in each half of the window (0-9] and (9-18]
CD4 Case: 50% of infection probability is reallocated to the CD4-based median window
Note that for cases with CD4 < 200, there is no change from the Base Case.
| CD4 Category | CD4 Median | Impacts windows longer than |
|---|---|---|
| >500 | 1.5 | 3.0 |
| 350-500 | 4.0 | 8.0 |
| 200-350 | 8.0 | 16.0 |
Actual impact will depend on how much longer windows are than 2x the CD4-based median
Why: We exclude cases when their first CD4 count is not within 30 days of HIV Dx, or if it's missing
Treatment is likely to begin soon after diagnosis, altering CD4 counts
Note the longer the delay, the higher the CD4 count, which is consistent with initiation of treatment.
It may be possible to refine this exclusion by using info on treatment initiation & viral load.
For those with a valid first CD4 measure (N=2178)
Recall that Never Testers get a window length of min(age-16, 18) years.
Note how much variability there is, even for the short-window cases!
** Confirms CD4 is a very noisy (i.e., non-informative) measure of recency
Shaded area indicates CD4 < 500 in never-testers.
Suggests delayed testing, maybe in response to HIV illness-related symptoms?
Implies the CD4 data will not modify the estimates for most of these long-window cases.
Shaded area indicates CD4 > 500 in never-testers
This is where the CD4 case has the greatest potential impact (if the never-testers are older)
Cases are modified if their observed window is at least 2x longer than the CD4-based median window
more common among non-MSM: 5%, or 5/17 = 29% of modified cases
than MSM: 1%, or 1/7 = 14% of modified cases
Median time since infection = time by which 50% of infection probability has occurred
Among all cases with testing history (N = 3016)
Declines are greater among the groups with higher CD4 levels.
This is what we expected, that the CD4 information would have less impact on MSM.
This result is a simple product of the proportion of cases modified, and the average change, for each group
Contributions to the absolute difference in median time since infection by group.
| Population | % Impacted | Average Change | Abs. Difference |
|---|---|---|---|
| MSM | 0.07 | -3.10 | -0.22 |
| non-MSM | 0.17 | -3.01 | -0.51 |
Among all cases with testing history (N = 3016)
This was a bit surprising
Why are they the same?
Contributions to the relative difference in median time since infection by group.
| Population | Base Case | Abs. Difference | Rel. Difference |
|---|---|---|---|
| MSM | 1.94 | -0.22 | -11.3 |
| non-MSM | 4.50 | -0.51 | -11.3 |
Average time undiagnosed = the mean of the TID curve
Mean TID for the Base Case and CD4 Case, by MSM status
| Population | Base Case | CD4 Case | Difference | Percent Change |
|---|---|---|---|---|
| MSM | 1.83 | 1.72 | 0.11 | -6.13 |
| non-MSM | 4.38 | 4.13 | 0.25 | -5.74 |
Undiagnosed cases
Subgroups parallel the decreases in mean undiagnosed time (6.1% for MSM and 5.7% for non-MSM)
| Population | Base Case | CD4 Case | Difference | Percent Change |
|---|---|---|---|---|
| Total | 1319.0 | 1247.0 | -72.0 | -5.5 |
| MSM | 604.7 | 568.4 | -36.3 | -6.0 |
| non-MSM | 714.3 | 678.2 | -36.1 | -5.1 |
Undiagnosed fractions
Here the MSM declines are relatively larger: -6.5 vs. -4.0 for non-MSM
| Population | Base Case | CD4 Case | Absolute Difference | Percent Change |
|---|---|---|---|---|
| Total | 9.4 | 8.9 | -0.5 | -5.3 |
| MSM | 6.2 | 5.8 | -0.4 | -6.5 |
| non-MSM | 17.1 | 16.4 | -0.7 | -4.0 |
In WA, most cases with long windows were not recently infected
Only 10% of cases with testing history had infection windows that indicated less probability of recent infection than indicated by their CD4 count
In WA, CD4 data incorporation had the same impact on both MSM and non-MSM estimates
We expected to see greater impact in non-MSM than MSM
This was due to offsetting differences in the three components that influenced the estimate:
Percent modified: MSM had a lower fraction of cases modified ** 7% for MSM vs 17% for non-MSM
Size of modification: MSM had larger decreases in their median time since infection ** -2.3 yrs for MSM vs -1.9 yrs for non-MSM
Starting values from Base Case estimates: MSM had lower base case estimates ** 6.2% for MSM vs 9.4% for non-MSM
This translated into slightly higher impacts on mean TID and mean undiagnosed estimates for MSM
We prioritize testing history data
We use CD4 conservatively
Our estimates now may be downwardly biased
So we do not recommend using the CD4-based estimates for publication
Recall that we are still using the “missing at random” assumption for cases without a testing history
We could also use CD4 data in cases with missing testing history
Indicators of recent infection by testing history status
| Non-Missing | Missing | |
|---|---|---|
| Percent BED + | 23 | 8 |
| Mean CD4 | 395 | 328 |
| Median CD4 | 372 | 280 |
| Percent with Concurrent Dx | 30 | 42 |
Longer term: We want to integrate multiple markers
Mean decrease in median time since infection is slighly larger for MSM (3.10 years) than for non-MSM (3.01 years).
Median decrease in median time since infection is slighly larger for MSM (2.34 years) than for non-MSM (1.94 years).
MSM
non-MSM